A Cretaceous carbonate reservoir, deposited in a shoal complex environment, produced only 10% of the estimated STOIIP, yet currently suffering from a rapid reservoir pressure decline. Recently acquired geoscience and engineering data revealed a lot of subsurface uncertainties. To boost the production and support a pressure maintenance project, a reviewed reservoir evaluation was critical to narrow down the uncertainties on reservoir structure, Tarmat prediction, rock quality, oil distribution and connectivity of aquifer with neighboring fields.
Owing to lateral variation of depositional environments field-wise and a complex diagenetic processes history, mutli-scale heterogeneities are seen both vertically and areally. Capitalizing on limited dataset, collected from early development wells and previously overseen deep exploration wells, a fully integrated approach was required to address these heterogeneities. Fault data, integrated with log & pressure data analysis and seismically mappable Tarmat related flat spots enabled to decipher reservoir compartmentalization. Detailed property modeling used a hybrid hierarchical workflow of deterministic, statistical and stochastic techniques, and allowed capturing depositional/diagenetic rock quality variations, including seismically mappable Tarmats' overprint, calibrated with well and geochemical data.
At borehole-scale, a cascaded PCA-NNs methodology in a hierarchical order yielded the best results in predicting lithofacies at un-cored intervals using wireline logs, thus enabling more favorably the comparison with the benchmark charts than the clusters generated by directly using NN with the same original logs.
Starting from a statistically proven tight relationship between borehole lithofacies, reservoir rock types and porosity, well-calibrated inverted seismic porosity maps have been used in combination with their corresponding kriged lithofacies proportion maps, together with well/seismic based variography analysis and sedimentological/stratigraphical concepts, to generate lithofacies trend maps. Thesemaps will be the main input we used for 3D facies distribution at the field scale.
The quantification of lithofacies statistical correspondances between well and seismic inversion data, enabled to segregate between reservoir shoal facies (porous limestone), non-reservoir facies (tight limestone), and intermediate-quality facies (fine-grained packestone). Seismic-scale sedimentary/diagenetic bodies were explicitly integrated into the facies model, however high-resolution borehole facies were stochastically populated, through constraining them to pre-established lithofacies trend maps. This served to directly constrain the 3D porosity distribution and, in turn, reservoir rock types - integrating lithology, petrophysics and reservoir behavior - all closely linked to each other.